{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\mpl_finance.py:16: DeprecationWarning: \n",
      "\n",
      "  =================================================================\n",
      "\n",
      "   WARNING: `mpl_finance` is deprecated:\n",
      "\n",
      "    Please use `mplfinance` instead (no hyphen, no underscore).\n",
      "\n",
      "    To install: `pip install --upgrade mplfinance` \n",
      "\n",
      "   For more information, see: https://pypi.org/project/mplfinance/\n",
      "\n",
      "  =================================================================\n",
      "\n",
      "  __warnings.warn('\\n\\n  ================================================================='+\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "from influxdb import InfluxDBClient\n",
    "import talib as  ta\n",
    "import warnings\n",
    "from matplotlib import pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from influxdb import InfluxDBClient\n",
    "import talib as  ta\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import warnings\n",
    "from matplotlib import pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "from common import candle\n",
    "client = InfluxDBClient('192.168.3.108',8086)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    \n",
    "def get_df_smas(df, lenthlist):\n",
    "    df_temp = pd.DataFrame(df['close'])\n",
    "    for i in lenthlist:\n",
    "        df_temp['sma_'+ str(i)] = ta.SMA(df['close'], i)\n",
    "    \n",
    "    return df_temp\n",
    "\n",
    "def get_df_smas_withoutclose(df, lenthlist):\n",
    "    df_temp = pd.DataFrame(df['time'])\n",
    "    \n",
    "    for i in lenthlist:\n",
    "        df_temp['sma_'+ str(i)] = talib.SMA(df['close'], i)\n",
    "    \n",
    "    return df_temp\n",
    "\n",
    "def execute(sql):\n",
    "    import pymysql\n",
    "    try:\n",
    "        db = pymysql.connect(host= \"192.168.3.144\",\n",
    "                         user=\"gmgbj\",\n",
    "                         passwd='G7gIVYB9Xk^7',\n",
    "                         port=3306,\n",
    "                         charset='utf8',\n",
    "                        db='strategy')\n",
    "\n",
    "        cursor = db.cursor()\n",
    "\n",
    "        # 使用 execute()  方法执行 SQL 查询\n",
    "        cursor.execute(sql)\n",
    "        db.commit()\n",
    "    except Exception as e:\n",
    "        print(sql) \n",
    "        print(e)\n",
    "        # 关闭数据库连接\n",
    "        db.rollback()\n",
    "        db.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_time = datetime.datetime.now()\n",
    "current_time\n",
    "dbname = \"KlineIndex\"\n",
    "\n",
    "sqlmin = \"select * from rp_month.KlineMin1 where symbol='HSI' and  time > '{}-{}-{} 09:00:00'\".format(\n",
    "        current_time.year,\n",
    "        str(current_time.month).rjust(2, '0'),\n",
    "        str(current_time.day).rjust(2, '0'))\n",
    "\n",
    "sqlday = \"select * from KlineDay where symbol='HSI' and  time > '2019-12-01 00:00:00'\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsmin = client.query(sqlmin, database=dbname)\n",
    "df_min = pd.DataFrame(rsmin.get_points())\n",
    "df_min  = init_min_df(df_min)\n",
    "\n",
    "df_min_brand = get_min_bbands(df_min)\n",
    "df_min_smas = get_df_smas(df_min, [5,30,60])\n",
    "df_min_all = pd.merge(df_min_smas, df_min_brand, on='time')\n",
    "#df_all.index = df_brand.index\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsday = client.query(sqlday, database=dbname)\n",
    "df_day = pd.DataFrame(rsday.get_points())\n",
    "\n",
    "df_day['time'] = pd.to_datetime(df_day['time'])\n",
    "df_day.time = df_day.time.apply(lambda x: x.strftime(\"%Y-%m-%d\"))\n",
    "df_day.set_index('time', inplace=True)\n",
    "df_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>preclose</th>\n",
       "      <th>symbol</th>\n",
       "      <th>value</th>\n",
       "      <th>vol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>26444.72</td>\n",
       "      <td>26511.55</td>\n",
       "      <td>26393.09</td>\n",
       "      <td>26475.34</td>\n",
       "      <td>26346.49</td>\n",
       "      <td>HSI</td>\n",
       "      <td>6.706260e+10</td>\n",
       "      <td>67062599680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>26391.30</td>\n",
       "      <td>26424.12</td>\n",
       "      <td>26063.02</td>\n",
       "      <td>26315.97</td>\n",
       "      <td>26444.72</td>\n",
       "      <td>HSI</td>\n",
       "      <td>7.124794e+10</td>\n",
       "      <td>71247937536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>26062.56</td>\n",
       "      <td>26191.79</td>\n",
       "      <td>25995.15</td>\n",
       "      <td>26071.39</td>\n",
       "      <td>26391.30</td>\n",
       "      <td>HSI</td>\n",
       "      <td>7.185700e+10</td>\n",
       "      <td>71856996352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>26217.04</td>\n",
       "      <td>26300.51</td>\n",
       "      <td>26134.06</td>\n",
       "      <td>26300.51</td>\n",
       "      <td>26062.56</td>\n",
       "      <td>HSI</td>\n",
       "      <td>6.693327e+10</td>\n",
       "      <td>66933268480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>26487.17</td>\n",
       "      <td>26520.08</td>\n",
       "      <td>26309.34</td>\n",
       "      <td>26345.20</td>\n",
       "      <td>26217.04</td>\n",
       "      <td>HSI</td>\n",
       "      <td>7.680077e+10</td>\n",
       "      <td>76800770048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close      high       low      open  preclose symbol  \\\n",
       "time                                                                  \n",
       "2019-12-02  26444.72  26511.55  26393.09  26475.34  26346.49    HSI   \n",
       "2019-12-03  26391.30  26424.12  26063.02  26315.97  26444.72    HSI   \n",
       "2019-12-04  26062.56  26191.79  25995.15  26071.39  26391.30    HSI   \n",
       "2019-12-05  26217.04  26300.51  26134.06  26300.51  26062.56    HSI   \n",
       "2019-12-06  26487.17  26520.08  26309.34  26345.20  26217.04    HSI   \n",
       "\n",
       "                   value          vol  \n",
       "time                                   \n",
       "2019-12-02  6.706260e+10  67062599680  \n",
       "2019-12-03  7.124794e+10  71247937536  \n",
       "2019-12-04  7.185700e+10  71856996352  \n",
       "2019-12-05  6.693327e+10  66933268480  \n",
       "2019-12-06  7.680077e+10  76800770048  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_20</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>26444.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>26391.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>26062.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>26217.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>26487.17</td>\n",
       "      <td>26320.558</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close      sma_5  sma_20\n",
       "time                                   \n",
       "2019-12-02  26444.72        NaN     NaN\n",
       "2019-12-03  26391.30        NaN     NaN\n",
       "2019-12-04  26062.56        NaN     NaN\n",
       "2019-12-05  26217.04        NaN     NaN\n",
       "2019-12-06  26487.17  26320.558     NaN"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_day_smas = df_day_smas.dropna()\n",
    "df_day_smas = get_df_smas(df_day, [5,20])\n",
    "df_day_smas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_20</th>\n",
       "      <th>signal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>26444.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>26391.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-04</th>\n",
       "      <td>26062.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-05</th>\n",
       "      <td>26217.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-06</th>\n",
       "      <td>26487.17</td>\n",
       "      <td>26320.558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               close      sma_5  sma_20  signal\n",
       "time                                           \n",
       "2019-12-02  26444.72        NaN     NaN       0\n",
       "2019-12-03  26391.30        NaN     NaN       0\n",
       "2019-12-04  26062.56        NaN     NaN       0\n",
       "2019-12-05  26217.04        NaN     NaN       0\n",
       "2019-12-06  26487.17  26320.558     NaN       0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day_smas['signal'] = 0\n",
    "for i in range(20, len(df_day_smas) - 1,1):\n",
    "    # print(i)\n",
    "    if(np.all([df_day_smas.sma_5[i-1] < df_day_smas.sma_20[i-1], df_day_smas.sma_5[i+1] > df_day_smas.sma_20[i+1]]) * 1): # 上穿\n",
    "        df_day_smas['signal'][i] = 1\n",
    "    elif(np.all([df_day_smas.sma_5[i-1] > df_day_smas.sma_20[i-1], df_day_smas.sma_5[i+1] < df_day_smas.sma_20[i+1]]) * 1):\n",
    "        df_day_smas['signal'][i] = -1\n",
    "    else:\n",
    "        df_day_smas['signal'][i] = 0\n",
    "\n",
    "\n",
    "df_day_smas.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_20</th>\n",
       "      <th>signal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>467.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26416.073383</td>\n",
       "      <td>26420.077214</td>\n",
       "      <td>26425.789325</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1951.350174</td>\n",
       "      <td>1933.698897</td>\n",
       "      <td>1884.683768</td>\n",
       "      <td>0.352794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21696.130000</td>\n",
       "      <td>22233.128000</td>\n",
       "      <td>23227.166000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24707.065000</td>\n",
       "      <td>24690.173000</td>\n",
       "      <td>24874.429625</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26301.480000</td>\n",
       "      <td>26333.458000</td>\n",
       "      <td>26314.927000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28296.735000</td>\n",
       "      <td>28337.287000</td>\n",
       "      <td>28428.480000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30632.640000</td>\n",
       "      <td>30451.578000</td>\n",
       "      <td>29679.358000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close         sma_5        sma_20      signal\n",
       "count    467.000000    463.000000    448.000000  467.000000\n",
       "mean   26416.073383  26420.077214  26425.789325    0.000000\n",
       "std     1951.350174   1933.698897   1884.683768    0.352794\n",
       "min    21696.130000  22233.128000  23227.166000   -1.000000\n",
       "25%    24707.065000  24690.173000  24874.429625    0.000000\n",
       "50%    26301.480000  26333.458000  26314.927000    0.000000\n",
       "75%    28296.735000  28337.287000  28428.480000    0.000000\n",
       "max    30632.640000  30451.578000  29679.358000    1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day_smas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "467"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_day_smas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210e5d68070>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ax = plt.figure(figsize=(16,8))\n",
    "df_day_smas[['close','sma_5','sma_20']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-12-02\n",
      "2019-12-03\n",
      "2019-12-04\n",
      "2019-12-05\n",
      "2019-12-06\n",
      "2019-12-09\n",
      "2019-12-10\n",
      "2019-12-11\n",
      "2019-12-12\n",
      "2019-12-13\n",
      "2019-12-16\n",
      "2019-12-17\n",
      "2019-12-18\n",
      "2019-12-19\n",
      "2019-12-20\n",
      "2019-12-23\n",
      "2019-12-24\n",
      "2019-12-27\n",
      "2019-12-30\n",
      "2019-12-31\n",
      "2020-01-02\n",
      "2020-01-03\n",
      "2020-01-06\n",
      "2020-01-07\n",
      "2020-01-08\n",
      "2020-01-09\n",
      "2020-01-10\n",
      "2020-01-13\n",
      "2020-01-14\n",
      "2020-01-15\n",
      "2020-01-16\n",
      "2020-01-17\n",
      "2020-01-20\n",
      "2020-01-21\n",
      "2020-01-22\n",
      "2020-01-23\n",
      "2020-01-24\n",
      "2020-01-29\n",
      "2020-01-30\n",
      "2020-01-31\n",
      "2020-02-03\n",
      "2020-02-04\n",
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      "2020-02-06\n",
      "2020-02-07\n",
      "2020-02-10\n",
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      "2020-03-02\n",
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      "2020-10-27\n",
      "2020-10-28\n",
      "2020-10-29\n",
      "2020-10-30\n",
      "2020-11-02\n",
      "2020-11-03\n",
      "2020-11-04\n",
      "2020-11-05\n",
      "2020-11-06\n",
      "2020-11-09\n",
      "2020-11-10\n",
      "2020-11-11\n",
      "2020-11-12\n",
      "2020-11-13\n",
      "2020-11-16\n",
      "2020-11-17\n",
      "2020-11-18\n",
      "2020-11-19\n",
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      "2020-11-25\n",
      "2020-11-26\n",
      "2020-11-27\n",
      "2020-11-30\n",
      "2020-12-01\n",
      "2020-12-02\n",
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      "2020-12-04\n",
      "2020-12-07\n",
      "2020-12-08\n",
      "2020-12-09\n",
      "2020-12-10\n",
      "2020-12-11\n",
      "2020-12-14\n",
      "2020-12-15\n",
      "2020-12-16\n",
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      "2020-12-18\n",
      "2020-12-21\n",
      "2020-12-22\n",
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      "2020-12-24\n",
      "2020-12-28\n",
      "2020-12-29\n",
      "2020-12-30\n",
      "2020-12-31\n",
      "2021-01-04\n",
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      "2021-09-01\n",
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      "2021-09-03\n",
      "2021-09-06\n",
      "2021-09-07\n",
      "2021-09-08\n",
      "2021-09-09\n",
      "2021-09-10\n",
      "2021-09-13\n",
      "2021-09-14\n",
      "2021-09-15\n",
      "2021-09-16\n",
      "2021-09-17\n",
      "2021-09-20\n",
      "2021-09-21\n",
      "2021-09-23\n",
      "2021-09-24\n",
      "2021-09-27\n",
      "2021-09-28\n",
      "2021-09-29\n",
      "2021-09-30\n",
      "2021-10-04\n",
      "2021-10-05\n",
      "2021-10-06\n",
      "2021-10-11\n",
      "2021-10-12\n",
      "2021-10-15\n",
      "2021-10-18\n",
      "2021-10-19\n",
      "2021-10-20\n",
      "2021-10-21\n",
      "2021-10-22\n",
      "2021-10-25\n",
      "2021-10-26\n",
      "2021-10-27\n",
      "2021-10-28\n",
      "2021-10-29\n"
     ]
    }
   ],
   "source": [
    "for idrow, row in df_day_smas.iterrows():\n",
    "    try:\n",
    "        print(idrow)\n",
    "        #print(row)\n",
    "        sql = 'insert into signalsinfo (hqdate, market, symbol, S2) values(\"{}\",\"{}\",\"{}\",\"{}\") ON DUPLICATE KEY UPDATE S2={}' \\\n",
    "            .format(idrow,'HK','HSI', str(row['signal']), str(row['signal']))\n",
    "        \n",
    "        # print(sql)\n",
    "        execute(sql)\n",
    "    except Exception as e:\n",
    "        print('error', e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_day_smas.index = pd.to_datetime(df_day_smas.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ret = pd.DataFrame(df['ret'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curve(df_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>preclose</th>\n",
       "      <th>symbol</th>\n",
       "      <th>value</th>\n",
       "      <th>vol</th>\n",
       "      <th>week</th>\n",
       "      <th>ret</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20210104</th>\n",
       "      <td>2021-01-04 00:00:00+00:00</td>\n",
       "      <td>27472.81</td>\n",
       "      <td>27502.83</td>\n",
       "      <td>27079.24</td>\n",
       "      <td>27087.13</td>\n",
       "      <td>27231.13</td>\n",
       "      <td>HSI</td>\n",
       "      <td>1.826409e+11</td>\n",
       "      <td>18483724163</td>\n",
       "      <td>1</td>\n",
       "      <td>385.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210111</th>\n",
       "      <td>2021-01-11 00:00:00+00:00</td>\n",
       "      <td>27908.22</td>\n",
       "      <td>28176.65</td>\n",
       "      <td>27794.81</td>\n",
       "      <td>28003.98</td>\n",
       "      <td>27878.22</td>\n",
       "      <td>HSI</td>\n",
       "      <td>2.541468e+11</td>\n",
       "      <td>23684851585</td>\n",
       "      <td>1</td>\n",
       "      <td>-95.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210118</th>\n",
       "      <td>2021-01-18 00:00:00+00:00</td>\n",
       "      <td>28862.77</td>\n",
       "      <td>28864.25</td>\n",
       "      <td>28390.29</td>\n",
       "      <td>28454.59</td>\n",
       "      <td>28573.86</td>\n",
       "      <td>HSI</td>\n",
       "      <td>2.243974e+11</td>\n",
       "      <td>21937671958</td>\n",
       "      <td>1</td>\n",
       "      <td>408.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210125</th>\n",
       "      <td>2021-01-25 00:00:00+00:00</td>\n",
       "      <td>30159.01</td>\n",
       "      <td>30191.16</td>\n",
       "      <td>29673.26</td>\n",
       "      <td>29677.30</td>\n",
       "      <td>29447.85</td>\n",
       "      <td>HSI</td>\n",
       "      <td>2.869093e+11</td>\n",
       "      <td>25934911400</td>\n",
       "      <td>1</td>\n",
       "      <td>481.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20210201</th>\n",
       "      <td>2021-02-01 00:00:00+00:00</td>\n",
       "      <td>28892.86</td>\n",
       "      <td>28982.77</td>\n",
       "      <td>28382.26</td>\n",
       "      <td>28457.85</td>\n",
       "      <td>28283.71</td>\n",
       "      <td>HSI</td>\n",
       "      <td>2.012190e+11</td>\n",
       "      <td>16810430752</td>\n",
       "      <td>1</td>\n",
       "      <td>435.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              time     close      high       low      open  \\\n",
       "date                                                                         \n",
       "20210104 2021-01-04 00:00:00+00:00  27472.81  27502.83  27079.24  27087.13   \n",
       "20210111 2021-01-11 00:00:00+00:00  27908.22  28176.65  27794.81  28003.98   \n",
       "20210118 2021-01-18 00:00:00+00:00  28862.77  28864.25  28390.29  28454.59   \n",
       "20210125 2021-01-25 00:00:00+00:00  30159.01  30191.16  29673.26  29677.30   \n",
       "20210201 2021-02-01 00:00:00+00:00  28892.86  28982.77  28382.26  28457.85   \n",
       "\n",
       "          preclose symbol         value          vol week     ret  \n",
       "date                                                               \n",
       "20210104  27231.13    HSI  1.826409e+11  18483724163    1  385.68  \n",
       "20210111  27878.22    HSI  2.541468e+11  23684851585    1  -95.76  \n",
       "20210118  28573.86    HSI  2.243974e+11  21937671958    1  408.18  \n",
       "20210125  29447.85    HSI  2.869093e+11  25934911400    1  481.71  \n",
       "20210201  28283.71    HSI  2.012190e+11  16810430752    1  435.01  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week1 = df[df['week'] == \"1\"]\n",
    "df_week1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 118 entries, 20210104 to 20210907\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype              \n",
      "---  ------    --------------  -----              \n",
      " 0   time      118 non-null    datetime64[ns, UTC]\n",
      " 1   close     118 non-null    float64            \n",
      " 2   high      118 non-null    float64            \n",
      " 3   low       118 non-null    float64            \n",
      " 4   open      118 non-null    float64            \n",
      " 5   preclose  118 non-null    float64            \n",
      " 6   symbol    118 non-null    object             \n",
      " 7   value     118 non-null    float64            \n",
      " 8   vol       118 non-null    int64              \n",
      " 9   week      118 non-null    object             \n",
      " 10  ret       118 non-null    float64            \n",
      "dtypes: datetime64[ns, UTC](1), float64(7), int64(1), object(2)\n",
      "memory usage: 11.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df_ret_100 = df[abs(df['ret']) > 100]\n",
    "df_ret_100.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    34\n",
       "3    34\n",
       "4    34\n",
       "5    33\n",
       "1    33\n",
       "Name: week, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['week'].value_counts()"
   ]
  }
 ],
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